Modeling in the oil and gas industry is important to maximizing return on investment. Such modeling includes the modeling of formations, as well as the modeling of drilling and extraction systems used to recover hydrocarbons from formations. One important aspect of any such model is to understand the effects of changes to various model parameters. For example, one might model how a change in drilling mud density might affect the drill string penetration rate. To better understand the effects of different parameters on an engineering system, such as a drill string, engineers often turn to sensitivity analysis.
Sensitivity analysis generally describes the process of determining how the projected outcome of a model will be affected by changing the model's input. Sensitivity analysis can provide important insight into the quality of a model and the reliability of the model's input. Sensitivity analysis is used to better manage risk in a wide variety of disciplines, such as engineering, chemistry, economics, finance and biostatistics.
Performing sensitivity analysis is a time intensive process. Typically, a user first must enter a set of input parameters, compute the results, and freeze a line on a plot corresponding to the input. The user then must change the input, compute a second set of results, and freeze a second line on the plot. The same steps must be repeated for each additional scenario the user wishes to analyze. Further, these steps become increasingly difficult with each additional parameter and value used in the analysis.
For example, well planning models are used to plan the drilling of wells in the oil and gas industries. Such models may include dozens of input parameters and sophisticated computer graphics that make sensitivity analysis a tedious and time-consuming process.